library(tigris)
## To enable 
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.4     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(sf)
## Linking to GEOS 3.8.1, GDAL 3.2.1, PROJ 7.2.1
library(mapview)
library(leaflet)
library(censusapi)
## 
## Attaching package: 'censusapi'
## The following object is masked from 'package:methods':
## 
##     getFunction
library(tidyverse)
years <- 2017:2021
quarters <- 1:4
types <- c("Electric","Gas")

pge_data_raw <- NULL

for(year in years){
  for(quarter in quarters){
    for(type in types){
      filename <-
        paste0(
          "PGE_",
          year,
          "_Q",
          quarter,
          "_",
          type,
          "UsageByZip.csv"
        )
        if(!file.exists(filename)) next
        temp <- read_csv(filename)
        if(type == "Electric"){
          temp<-
            temp%>%
            #conversion: 1 kWH = 3.41213 kBTU
            mutate(TOTALKBTU =  TOTALKWH*3.41214)%>%
            select(-TOTALKWH, -AVERAGEKWH)
        }
        if(type == "Gas") {
          temp <-
            temp %>%
            #conversion: 1 Therm = 99.9761 kBTU
            mutate(TOTALKBTU = TOTALTHM * 99.9761) %>%
            select(-TOTALTHM, -AVERAGETHM)
        }
        pge_data_raw <-
        rbind(pge_data_raw,temp)
    }
  }
}
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## Rows: 3502 Columns: 8
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## Rows: 7877 Columns: 8
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## Rows: 3503 Columns: 8
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## Rows: 7875 Columns: 8
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## Rows: 3496 Columns: 8
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## Rows: 7870 Columns: 8
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## Rows: 3499 Columns: 8
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## Rows: 7887 Columns: 8
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## Rows: 3499 Columns: 8
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## Rows: 7891 Columns: 8
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## Rows: 3489 Columns: 8
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## chr (2): CUSTOMERCLASS, COMBINED
## dbl (3): ZIPCODE, MONTH, YEAR
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## Rows: 7874 Columns: 8
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## dbl (3): ZIPCODE, MONTH, YEAR
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## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 3459 Columns: 8
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## chr (2): CUSTOMERCLASS, COMBINED
## dbl (3): ZIPCODE, MONTH, YEAR
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
pge_data <-
  pge_data_raw %>%
  filter(
    CUSTOMERCLASS %in% c("Elec- Residential","Gas- Residential","Elec- Commercial","Gas- Commercial")
  ) %>%
  group_by(
    YEAR,
    MONTH,
    CUSTOMERCLASS
  ) %>%
  summarize(
    TOTALKBTU = sum(TOTALKBTU, na.rm = T)
  ) %>%
  mutate(
    DATE = 
      paste(
        YEAR,
        MONTH,
        "01",
        sep="-"
      ) %>% as.Date()
  )
## `summarise()` has grouped output by 'YEAR', 'MONTH'. You can override using the `.groups` argument.
#line gragh 
pge_data %>%
  filter(CUSTOMERCLASS %in% c("Elec- Commercial","Gas- Commercial")) %>%
  ggplot() +
  geom_line(
    aes(
      x = DATE,
      y = TOTALKBTU,
      color = CUSTOMERCLASS
    ),
    stat = "identity",
    position = "stack"
  ) +
  labs(
    x = "Date",
    y = "kBTU",
    title = "PG&E Commercial Yearly Electricity & Gas Usage 2017-2021",
    color = "Energy Type"
  ) 

#line gragh
pge_data %>%
  filter(CUSTOMERCLASS %in% c("Elec- Residential","Gas- Residential")) %>%
  ggplot() +
  geom_line(
    aes(
      x = DATE,
      y = TOTALKBTU,
      color = CUSTOMERCLASS
    ),
    stat = "identity",
    position = "stack"
  ) +
  labs(
    x = "Date",
    y = "kBTU",
    title = "PG&E Residential Yearly Electricity & Gas Usage 2017-2021",
    color = "Energy Type"
  ) 

#bar gragh
library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
pge_residential<-
  pge_data %>%
  filter(CUSTOMERCLASS %in% c("Elec- Residential","Gas- Residential")) %>%
  ggplot() +
  geom_bar(
    aes(
      x = DATE,
      y = TOTALKBTU,
      fill = CUSTOMERCLASS
    ),
    stat = "identity",
    position = "stack"
  ) +
  labs(
    x = "Date",
    y = "kBTU",
    title = "PG&E Residential Yearly Electricity & Gas Usage 2017-2021",
    fill = "Energy Type"
  )
pge_residential %>%
  ggplotly()%>%
  layout(
    xaxis=list(fixedrange = T),
    yaxis = list(fixedrange = T)
  ) %>%
  config(displayModeBar = F)
#bar gragh
library(plotly)
pge_commercial<-
  pge_data %>%
  filter(CUSTOMERCLASS %in% c("Elec- Commercial","Gas- Commercial")) %>%
  ggplot() +
  geom_bar(
    aes(
      x = DATE,
      y = TOTALKBTU,
      fill = CUSTOMERCLASS
    ),
    stat = "identity",
    position = "stack"
  ) +
  labs(
    x = "Date",
    y = "kBTU",
    title = "PG&E Commercial Yearly Electricity & Gas Usage 2017-2021",
    fill = "Energy Type"
  )
pge_commercial %>%
  ggplotly()%>%
  layout(
    xaxis=list(fixedrange = T),
    yaxis = list(fixedrange = T)
  ) %>%
  config(displayModeBar = F)

#The covid epidemic did not appear to have a drastic impact on residental and commercial electricty use or gas use. There was a decline in the usage around early 2020 when most people were quarantining, but the lowest point in 2020 was not drastically lower than the lowest point in years before (2017-2019)

#A caveat of this data is that it is unclear how it was collected. The usage reports and Current Reports do not explain how energy measurments were made and whether customers were reporting the data themselves. I assumed that all the data in these sets were collected consistently and in an accurate manner. "

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